Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques also offers promising opportunities to automate many tasks in the telecommunication (telecom) field. After pre-training and fine-tuning, LLMs can perform diverse downstream tasks based on human instructions, paving the way to artificial general intelligence (AGI)-enabled 6G. Given the great potential of LLM technologies, this work aims to provide a comprehensive overview of LLM-enabled telecom networks. In particular, we first present LLM fundamentals, including model architecture, pre-training, fine-tuning, inference and utilization, model evaluation, and telecom deployment. Then, we introduce LLM-enabled key techniques and telecom applications in terms of generation, classification, optimization, and prediction problems. Specifically, the LLM-enabled generation applications include telecom domain knowledge, code, and network configuration generation. After that, the LLM-based classification applications involve network security, text, image, and traffic classification problems. Moreover, multiple LLM-enabled optimization techniques are introduced, such as automated reward function design for reinforcement learning and verbal reinforcement learning. Furthermore, for LLM-aided prediction problems, we discussed time-series prediction models and multi-modality prediction problems for telecom. Finally, we highlight the challenges and identify the future directions of LLM-enabled telecom networks.

翻译：大型语言模型（LLM）凭借其卓越的理解与推理能力，近年来受到广泛关注，并在众多领域取得了重大进展。LLM技术的进步也为电信领域诸多任务的自动化提供了广阔前景。经过预训练与微调后，LLM能够基于人类指令执行多样化的下游任务，为通向6G时代的人工通用智能（AGI）奠定了基础。鉴于LLM技术的巨大潜力，本文旨在系统综述LLM赋能的电信网络。具体而言，我们首先阐述LLM的基础原理，包括模型架构、预训练、微调、推理与应用、模型评估及电信部署方案。随后，我们从生成、分类、优化与预测四大问题类型出发，介绍LLM赋能的电信关键技术及应用场景。在生成应用方面，涵盖电信领域知识生成、代码生成与网络配置生成；在分类应用方面，涉及网络安全分类、文本分类、图像分类及流量分类问题；在优化技术方面，重点介绍基于LLM的强化学习自动奖励函数设计与语言强化学习方法；在预测问题方面，探讨面向电信场景的时间序列预测模型与多模态预测问题。最后，我们指出LLM赋能电信网络面临的挑战，并展望未来研究方向。